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In the recent study of Object Detection techniques, Deep Learning has an impact on development of the methodologies in the areas of Healthcare Industry such as cancer diagnosis, Medicine, Diabetic, Cardiac Imaging, Tumour Detection and many more. The recent study of object detection has made a comparison on utilisation of several Machine Learning models in the areas of healthcare. In this study, an attempt has been made to focus on the comparison between results of object detection from medical images using both YOLO (You Only Look Once) and CNN (Convolutional Neural Network). Classification techniques in computational demands and interpretability are reviewed in this study. This review allows the readers to understand the strengths and challenges across methodologies, thereby finding the way for informed decisions in real time applications. The study also attempts in addressing challenges like computational resources of image processing techniques. As the author of this paper, I appreciate the constructive feedback received through the double-blind peer review process, which significantly enhanced the clarity and depth of the research.

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Object Detection in the Healthcare Industry Using Yolo: A Better Way Than CNN

  • 章节

  • 发表日期：

    2025年

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  • Kaushik Das,
  • Arun Kr. Baruah

摘要

In the recent study of Object Detection techniques, Deep Learning has an impact on development of the methodologies in the areas of Healthcare Industry such as cancer diagnosis, Medicine, Diabetic, Cardiac Imaging, Tumour Detection and many more. The recent study of object detection has made a comparison on utilisation of several Machine Learning models in the areas of healthcare. In this study, an attempt has been made to focus on the comparison between results of object detection from medical images using both YOLO (You Only Look Once) and CNN (Convolutional Neural Network). Classification techniques in computational demands and interpretability are reviewed in this study. This review allows the readers to understand the strengths and challenges across methodologies, thereby finding the way for informed decisions in real time applications. The study also attempts in addressing challenges like computational resources of image processing techniques. As the author of this paper, I appreciate the constructive feedback received through the double-blind peer review process, which significantly enhanced the clarity and depth of the research.